Overview

Brought to you by YData

Dataset statistics

Number of variables15
Number of observations50000
Missing cells11299
Missing cells (%)1.5%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory5.7 MiB
Average record size in memory120.0 B

Variable types

Numeric4
Text7
Categorical4

Alerts

color is highly overall correlated with transmissionHigh correlation
odometer is highly overall correlated with sellingprice and 1 other fieldsHigh correlation
sellingprice is highly overall correlated with odometer and 1 other fieldsHigh correlation
state is highly overall correlated with transmissionHigh correlation
transmission is highly overall correlated with color and 1 other fieldsHigh correlation
year is highly overall correlated with odometer and 1 other fieldsHigh correlation
transmission is highly imbalanced (86.4%) Imbalance
interior is highly imbalanced (50.6%) Imbalance
make has 953 (1.9%) missing values Missing
model has 963 (1.9%) missing values Missing
trim has 988 (2.0%) missing values Missing
body has 1218 (2.4%) missing values Missing
transmission has 5986 (12.0%) missing values Missing
condition has 1077 (2.2%) missing values Missing

Reproduction

Analysis started2025-06-06 12:07:31.831775
Analysis finished2025-06-06 12:07:36.570076
Duration4.74 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

year
Real number (ℝ)

High correlation 

Distinct27
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2010.044
Minimum1986
Maximum2015
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size390.8 KiB
2025-06-06T12:07:36.638512image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Quantile statistics

Minimum1986
5-th percentile2002
Q12007
median2012
Q32013
95-th percentile2014
Maximum2015
Range29
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.9535929
Coefficient of variation (CV)0.0019669186
Kurtosis0.95061593
Mean2010.044
Median Absolute Deviation (MAD)2
Skewness-1.1784015
Sum1.005022 × 108
Variance15.630897
MonotonicityNot monotonic
2025-06-06T12:07:36.753709image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
2012 9253
18.5%
2013 8815
17.6%
2014 7243
14.5%
2011 4282
8.6%
2008 2794
 
5.6%
2007 2765
 
5.5%
2006 2483
 
5.0%
2010 2349
 
4.7%
2005 1925
 
3.9%
2009 1883
 
3.8%
Other values (17) 6208
12.4%
ValueCountFrequency (%)
1986 1
 
< 0.1%
1990 3
 
< 0.1%
1991 5
 
< 0.1%
1992 10
 
< 0.1%
1993 19
 
< 0.1%
1994 43
 
0.1%
1995 66
 
0.1%
1996 84
0.2%
1997 140
0.3%
1998 167
0.3%
ValueCountFrequency (%)
2015 793
 
1.6%
2014 7243
14.5%
2013 8815
17.6%
2012 9253
18.5%
2011 4282
8.6%
2010 2349
 
4.7%
2009 1883
 
3.8%
2008 2794
 
5.6%
2007 2765
 
5.5%
2006 2483
 
5.0%

make
Text

Missing 

Distinct80
Distinct (%)0.2%
Missing953
Missing (%)1.9%
Memory size390.8 KiB
2025-06-06T12:07:36.897002image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Length

Max length13
Median length11
Mean length6.0031195
Min length2

Characters and Unicode

Total characters294435
Distinct characters49
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique10 ?
Unique (%)< 0.1%

Sample

1st rowFord
2nd rowInfiniti
3rd rowJeep
4th rowFord
5th rowKia
ValueCountFrequency (%)
ford 8480
17.2%
chevrolet 5391
 
11.0%
nissan 4749
 
9.6%
toyota 3627
 
7.4%
dodge 2750
 
5.6%
honda 2473
 
5.0%
hyundai 1926
 
3.9%
bmw 1868
 
3.8%
mercedes-benz 1623
 
3.3%
kia 1578
 
3.2%
Other values (47) 14763
30.0%
2025-06-06T12:07:37.163748image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
o 29512
 
10.0%
e 27145
 
9.2%
a 20894
 
7.1%
r 20590
 
7.0%
d 19455
 
6.6%
n 16655
 
5.7%
i 16557
 
5.6%
s 15864
 
5.4%
t 11542
 
3.9%
l 10315
 
3.5%
Other values (39) 105906
36.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 294435
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 29512
 
10.0%
e 27145
 
9.2%
a 20894
 
7.1%
r 20590
 
7.0%
d 19455
 
6.6%
n 16655
 
5.7%
i 16557
 
5.6%
s 15864
 
5.4%
t 11542
 
3.9%
l 10315
 
3.5%
Other values (39) 105906
36.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 294435
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 29512
 
10.0%
e 27145
 
9.2%
a 20894
 
7.1%
r 20590
 
7.0%
d 19455
 
6.6%
n 16655
 
5.7%
i 16557
 
5.6%
s 15864
 
5.4%
t 11542
 
3.9%
l 10315
 
3.5%
Other values (39) 105906
36.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 294435
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 29512
 
10.0%
e 27145
 
9.2%
a 20894
 
7.1%
r 20590
 
7.0%
d 19455
 
6.6%
n 16655
 
5.7%
i 16557
 
5.6%
s 15864
 
5.4%
t 11542
 
3.9%
l 10315
 
3.5%
Other values (39) 105906
36.0%

model
Text

Missing 

Distinct751
Distinct (%)1.5%
Missing963
Missing (%)1.9%
Memory size390.8 KiB
2025-06-06T12:07:37.396204image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Length

Max length24
Median length22
Mean length6.7469258
Min length1

Characters and Unicode

Total characters330849
Distinct characters65
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique104 ?
Unique (%)0.2%

Sample

1st rowFusion
2nd rowJX
3rd rowGrand Cherokee
4th rowF-350 Super Duty
5th rowSportage
ValueCountFrequency (%)
altima 1691
 
2.8%
series 1385
 
2.3%
f-150 1306
 
2.2%
grand 1302
 
2.2%
1500 1259
 
2.1%
fusion 1238
 
2.1%
camry 1224
 
2.1%
escape 1135
 
1.9%
focus 968
 
1.6%
g 861
 
1.4%
Other values (626) 47329
79.3%
2025-06-06T12:07:37.789376image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 33422
 
10.1%
r 24505
 
7.4%
e 24148
 
7.3%
o 17441
 
5.3%
n 16346
 
4.9%
i 15070
 
4.6%
s 13676
 
4.1%
t 12025
 
3.6%
l 11941
 
3.6%
C 11140
 
3.4%
Other values (55) 151135
45.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 330849
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 33422
 
10.1%
r 24505
 
7.4%
e 24148
 
7.3%
o 17441
 
5.3%
n 16346
 
4.9%
i 15070
 
4.6%
s 13676
 
4.1%
t 12025
 
3.6%
l 11941
 
3.6%
C 11140
 
3.4%
Other values (55) 151135
45.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 330849
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 33422
 
10.1%
r 24505
 
7.4%
e 24148
 
7.3%
o 17441
 
5.3%
n 16346
 
4.9%
i 15070
 
4.6%
s 13676
 
4.1%
t 12025
 
3.6%
l 11941
 
3.6%
C 11140
 
3.4%
Other values (55) 151135
45.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 330849
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 33422
 
10.1%
r 24505
 
7.4%
e 24148
 
7.3%
o 17441
 
5.3%
n 16346
 
4.9%
i 15070
 
4.6%
s 13676
 
4.1%
t 12025
 
3.6%
l 11941
 
3.6%
C 11140
 
3.4%
Other values (55) 151135
45.7%

trim
Text

Missing 

Distinct1222
Distinct (%)2.5%
Missing988
Missing (%)2.0%
Memory size390.8 KiB
2025-06-06T12:07:38.074982image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Length

Max length46
Median length36
Mean length4.7444095
Min length1

Characters and Unicode

Total characters232533
Distinct characters71
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique336 ?
Unique (%)0.7%

Sample

1st rowSE
2nd rowJX35
3rd rowLaredo
4th rowXLT
5th rowLX
ValueCountFrequency (%)
base 4979
 
8.3%
se 4488
 
7.5%
s 2648
 
4.4%
lx 1866
 
3.1%
lt 1779
 
3.0%
limited 1771
 
2.9%
xlt 1734
 
2.9%
2.5 1625
 
2.7%
ls 1625
 
2.7%
sport 1581
 
2.6%
Other values (707) 36125
60.0%
2025-06-06T12:07:38.491822image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
L 19240
 
8.3%
S 18493
 
8.0%
e 13762
 
5.9%
i 11933
 
5.1%
E 11581
 
5.0%
11209
 
4.8%
T 10716
 
4.6%
a 9740
 
4.2%
r 8838
 
3.8%
X 8207
 
3.5%
Other values (61) 108814
46.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 232533
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
L 19240
 
8.3%
S 18493
 
8.0%
e 13762
 
5.9%
i 11933
 
5.1%
E 11581
 
5.0%
11209
 
4.8%
T 10716
 
4.6%
a 9740
 
4.2%
r 8838
 
3.8%
X 8207
 
3.5%
Other values (61) 108814
46.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 232533
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
L 19240
 
8.3%
S 18493
 
8.0%
e 13762
 
5.9%
i 11933
 
5.1%
E 11581
 
5.0%
11209
 
4.8%
T 10716
 
4.6%
a 9740
 
4.2%
r 8838
 
3.8%
X 8207
 
3.5%
Other values (61) 108814
46.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 232533
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
L 19240
 
8.3%
S 18493
 
8.0%
e 13762
 
5.9%
i 11933
 
5.1%
E 11581
 
5.0%
11209
 
4.8%
T 10716
 
4.6%
a 9740
 
4.2%
r 8838
 
3.8%
X 8207
 
3.5%
Other values (61) 108814
46.8%

body
Text

Missing 

Distinct73
Distinct (%)0.1%
Missing1218
Missing (%)2.4%
Memory size390.8 KiB
2025-06-06T12:07:38.620097image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Length

Max length23
Median length5
Mean length5.2589275
Min length3

Characters and Unicode

Total characters256541
Distinct characters48
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique9 ?
Unique (%)< 0.1%

Sample

1st rowSedan
2nd rowSUV
3rd rowSUV
4th rowCrew Cab
5th rowSUV
ValueCountFrequency (%)
sedan 22339
42.3%
suv 12910
24.5%
cab 2901
 
5.5%
hatchback 2281
 
4.3%
minivan 2265
 
4.3%
coupe 1835
 
3.5%
crew 1455
 
2.8%
wagon 1395
 
2.6%
convertible 953
 
1.8%
g 861
 
1.6%
Other values (31) 3571
 
6.8%
2025-06-06T12:07:38.889522image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 35376
13.8%
e 31540
12.3%
S 30414
11.9%
n 30269
11.8%
d 23488
 
9.2%
V 11241
 
4.4%
U 10727
 
4.2%
C 6998
 
2.7%
b 6756
 
2.6%
s 6555
 
2.6%
Other values (38) 63177
24.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 256541
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 35376
13.8%
e 31540
12.3%
S 30414
11.9%
n 30269
11.8%
d 23488
 
9.2%
V 11241
 
4.4%
U 10727
 
4.2%
C 6998
 
2.7%
b 6756
 
2.6%
s 6555
 
2.6%
Other values (38) 63177
24.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 256541
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 35376
13.8%
e 31540
12.3%
S 30414
11.9%
n 30269
11.8%
d 23488
 
9.2%
V 11241
 
4.4%
U 10727
 
4.2%
C 6998
 
2.7%
b 6756
 
2.6%
s 6555
 
2.6%
Other values (38) 63177
24.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 256541
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 35376
13.8%
e 31540
12.3%
S 30414
11.9%
n 30269
11.8%
d 23488
 
9.2%
V 11241
 
4.4%
U 10727
 
4.2%
C 6998
 
2.7%
b 6756
 
2.6%
s 6555
 
2.6%
Other values (38) 63177
24.6%

transmission
Categorical

High correlation  Imbalance  Missing 

Distinct3
Distinct (%)< 0.1%
Missing5986
Missing (%)12.0%
Memory size390.8 KiB
automatic
42510 
manual
 
1502
Sedan
 
2

Length

Max length9
Median length9
Mean length8.8974417
Min length5

Characters and Unicode

Total characters391612
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowautomatic
2nd rowautomatic
3rd rowautomatic
4th rowautomatic
5th rowautomatic

Common Values

ValueCountFrequency (%)
automatic 42510
85.0%
manual 1502
 
3.0%
Sedan 2
 
< 0.1%
(Missing) 5986
 
12.0%

Length

2025-06-06T12:07:39.029033image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-06T12:07:39.143702image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
ValueCountFrequency (%)
automatic 42510
96.6%
manual 1502
 
3.4%
sedan 2
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
a 88026
22.5%
t 85020
21.7%
u 44012
11.2%
m 44012
11.2%
o 42510
10.9%
i 42510
10.9%
c 42510
10.9%
n 1504
 
0.4%
l 1502
 
0.4%
S 2
 
< 0.1%
Other values (2) 4
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 391612
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 88026
22.5%
t 85020
21.7%
u 44012
11.2%
m 44012
11.2%
o 42510
10.9%
i 42510
10.9%
c 42510
10.9%
n 1504
 
0.4%
l 1502
 
0.4%
S 2
 
< 0.1%
Other values (2) 4
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 391612
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 88026
22.5%
t 85020
21.7%
u 44012
11.2%
m 44012
11.2%
o 42510
10.9%
i 42510
10.9%
c 42510
10.9%
n 1504
 
0.4%
l 1502
 
0.4%
S 2
 
< 0.1%
Other values (2) 4
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 391612
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 88026
22.5%
t 85020
21.7%
u 44012
11.2%
m 44012
11.2%
o 42510
10.9%
i 42510
10.9%
c 42510
10.9%
n 1504
 
0.4%
l 1502
 
0.4%
S 2
 
< 0.1%
Other values (2) 4
 
< 0.1%

vin
Text

Distinct49925
Distinct (%)99.9%
Missing1
Missing (%)< 0.1%
Memory size390.8 KiB
2025-06-06T12:07:39.302861image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Length

Max length17
Median length17
Mean length16.99984
Min length9

Characters and Unicode

Total characters849975
Distinct characters35
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique49851 ?
Unique (%)99.7%

Sample

1st row3fa6p0h71dr236627
2nd row5n1al0mn8dc329790
3rd row1j8gr48k69c529636
4th row1ftww31rx8ed43632
5th rowkndpbcac3e7642651
ValueCountFrequency (%)
yv1ls5574w1496928 2
 
< 0.1%
kl8cd6s94dc512342 2
 
< 0.1%
19xfb2f85ee234128 2
 
< 0.1%
yv4982cz1a1553190 2
 
< 0.1%
1ft8x3bt1bea35997 2
 
< 0.1%
kndmb233266067038 2
 
< 0.1%
1gkdt13s562114606 2
 
< 0.1%
1j4ga59167l119551 2
 
< 0.1%
jtdkb20u467506884 2
 
< 0.1%
5tddk3eh3cs112711 2
 
< 0.1%
Other values (49915) 49979
> 99.9%
2025-06-06T12:07:39.625233image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 82378
 
9.7%
2 57285
 
6.7%
3 54734
 
6.4%
5 53261
 
6.3%
4 51475
 
6.1%
0 44621
 
5.2%
6 43745
 
5.1%
7 40753
 
4.8%
8 40378
 
4.8%
c 34327
 
4.0%
Other values (25) 347018
40.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 849975
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 82378
 
9.7%
2 57285
 
6.7%
3 54734
 
6.4%
5 53261
 
6.3%
4 51475
 
6.1%
0 44621
 
5.2%
6 43745
 
5.1%
7 40753
 
4.8%
8 40378
 
4.8%
c 34327
 
4.0%
Other values (25) 347018
40.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 849975
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 82378
 
9.7%
2 57285
 
6.7%
3 54734
 
6.4%
5 53261
 
6.3%
4 51475
 
6.1%
0 44621
 
5.2%
6 43745
 
5.1%
7 40753
 
4.8%
8 40378
 
4.8%
c 34327
 
4.0%
Other values (25) 347018
40.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 849975
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 82378
 
9.7%
2 57285
 
6.7%
3 54734
 
6.4%
5 53261
 
6.3%
4 51475
 
6.1%
0 44621
 
5.2%
6 43745
 
5.1%
7 40753
 
4.8%
8 40378
 
4.8%
c 34327
 
4.0%
Other values (25) 347018
40.8%

state
Categorical

High correlation 

Distinct40
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size390.8 KiB
fl
7379 
ca
6581 
pa
4879 
tx
4055 
ga
3214 
Other values (35)
23892 

Length

Max length17
Median length2
Mean length2.0006
Min length2

Characters and Unicode

Total characters100030
Distinct characters35
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)< 0.1%

Sample

1st rowwi
2nd rowca
3rd rowne
4th rowwa
5th rownv

Common Values

ValueCountFrequency (%)
fl 7379
14.8%
ca 6581
13.2%
pa 4879
 
9.8%
tx 4055
 
8.1%
ga 3214
 
6.4%
nj 2404
 
4.8%
il 2124
 
4.2%
oh 1986
 
4.0%
nc 1937
 
3.9%
tn 1867
 
3.7%
Other values (30) 13574
27.1%

Length

2025-06-06T12:07:39.767450image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
fl 7379
14.8%
ca 6581
13.2%
pa 4879
 
9.8%
tx 4055
 
8.1%
ga 3214
 
6.4%
nj 2404
 
4.8%
il 2124
 
4.2%
oh 1986
 
4.0%
nc 1937
 
3.9%
tn 1867
 
3.7%
Other values (30) 13574
27.1%

Most occurring characters

ValueCountFrequency (%)
a 17997
18.0%
n 9740
9.7%
c 9716
9.7%
l 9694
9.7%
f 7381
 
7.4%
t 6082
 
6.1%
m 5461
 
5.5%
p 5123
 
5.1%
i 4871
 
4.9%
o 4555
 
4.6%
Other values (25) 19410
19.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 100030
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 17997
18.0%
n 9740
9.7%
c 9716
9.7%
l 9694
9.7%
f 7381
 
7.4%
t 6082
 
6.1%
m 5461
 
5.5%
p 5123
 
5.1%
i 4871
 
4.9%
o 4555
 
4.6%
Other values (25) 19410
19.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 100030
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 17997
18.0%
n 9740
9.7%
c 9716
9.7%
l 9694
9.7%
f 7381
 
7.4%
t 6082
 
6.1%
m 5461
 
5.5%
p 5123
 
5.1%
i 4871
 
4.9%
o 4555
 
4.6%
Other values (25) 19410
19.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 100030
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 17997
18.0%
n 9740
9.7%
c 9716
9.7%
l 9694
9.7%
f 7381
 
7.4%
t 6082
 
6.1%
m 5461
 
5.5%
p 5123
 
5.1%
i 4871
 
4.9%
o 4555
 
4.6%
Other values (25) 19410
19.4%

condition
Real number (ℝ)

Missing 

Distinct41
Distinct (%)0.1%
Missing1077
Missing (%)2.2%
Infinite0
Infinite (%)0.0%
Mean30.619422
Minimum1
Maximum49
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size390.8 KiB
2025-06-06T12:07:39.889747image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q123
median35
Q342
95-th percentile47
Maximum49
Range48
Interquartile range (IQR)19

Descriptive statistics

Standard deviation13.471054
Coefficient of variation (CV)0.43995128
Kurtosis-0.24453918
Mean30.619422
Median Absolute Deviation (MAD)8
Skewness-0.82854595
Sum1497994
Variance181.4693
MonotonicityNot monotonic
2025-06-06T12:07:40.157651image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram with fixed size bins (bins=41)
ValueCountFrequency (%)
19 3723
 
7.4%
35 2382
 
4.8%
37 2307
 
4.6%
44 2256
 
4.5%
43 2193
 
4.4%
42 2165
 
4.3%
36 2049
 
4.1%
41 2036
 
4.1%
2 1940
 
3.9%
39 1816
 
3.6%
Other values (31) 26056
52.1%
ValueCountFrequency (%)
1 679
 
1.4%
2 1940
3.9%
3 969
1.9%
4 1804
3.6%
5 983
2.0%
11 12
 
< 0.1%
12 11
 
< 0.1%
13 6
 
< 0.1%
14 17
 
< 0.1%
15 15
 
< 0.1%
ValueCountFrequency (%)
49 1163
2.3%
48 1143
2.3%
47 1057
2.1%
46 1155
2.3%
45 1120
2.2%
44 2256
4.5%
43 2193
4.4%
42 2165
4.3%
41 2036
4.1%
39 1816
3.6%

odometer
Real number (ℝ)

High correlation 

Distinct41966
Distinct (%)83.9%
Missing5
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean68103.882
Minimum1
Maximum999999
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size390.8 KiB
2025-06-06T12:07:40.294375image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile10682.7
Q128312.5
median51806
Q398735.5
95-th percentile169696.8
Maximum999999
Range999998
Interquartile range (IQR)70423

Descriptive statistics

Standard deviation53485.779
Coefficient of variation (CV)0.78535581
Kurtosis15.904662
Mean68103.882
Median Absolute Deviation (MAD)30041
Skewness1.9824012
Sum3.4048536 × 109
Variance2.8607286 × 109
MonotonicityNot monotonic
2025-06-06T12:07:40.436658image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 114
 
0.2%
999999 7
 
< 0.1%
23870 6
 
< 0.1%
10936 5
 
< 0.1%
19940 5
 
< 0.1%
24088 5
 
< 0.1%
35546 5
 
< 0.1%
29184 5
 
< 0.1%
20161 5
 
< 0.1%
23720 5
 
< 0.1%
Other values (41956) 49833
99.7%
ValueCountFrequency (%)
1 114
0.2%
4 1
 
< 0.1%
5 2
 
< 0.1%
6 2
 
< 0.1%
10 3
 
< 0.1%
11 3
 
< 0.1%
12 1
 
< 0.1%
14 1
 
< 0.1%
15 1
 
< 0.1%
21 1
 
< 0.1%
ValueCountFrequency (%)
999999 7
< 0.1%
980113 1
 
< 0.1%
537334 1
 
< 0.1%
471114 1
 
< 0.1%
451641 1
 
< 0.1%
445317 1
 
< 0.1%
406777 1
 
< 0.1%
398532 1
 
< 0.1%
382200 1
 
< 0.1%
373909 1
 
< 0.1%

color
Categorical

High correlation 

Distinct21
Distinct (%)< 0.1%
Missing54
Missing (%)0.1%
Memory size390.8 KiB
black
9882 
white
9510 
gray
7572 
silver
7444 
blue
4600 
Other values (16)
10938 

Length

Max length9
Median length8
Mean length4.6206103
Min length1

Characters and Unicode

Total characters230781
Distinct characters30
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st rowwhite
2nd rowgray
3rd rowsilver
4th rowwhite
5th rowwhite

Common Values

ValueCountFrequency (%)
black 9882
19.8%
white 9510
19.0%
gray 7572
15.1%
silver 7444
14.9%
blue 4600
9.2%
red 3926
 
7.9%
2213
 
4.4%
green 1041
 
2.1%
gold 976
 
2.0%
beige 782
 
1.6%
Other values (11) 2000
 
4.0%

Length

2025-06-06T12:07:40.575400image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
black 9882
19.8%
white 9510
19.0%
gray 7572
15.2%
silver 7444
14.9%
blue 4600
9.2%
red 3926
 
7.9%
2213
 
4.4%
green 1041
 
2.1%
gold 976
 
2.0%
beige 782
 
1.6%
Other values (11) 2000
 
4.0%

Most occurring characters

ValueCountFrequency (%)
e 29764
12.9%
l 23319
 
10.1%
r 21709
 
9.4%
i 17915
 
7.8%
a 17730
 
7.7%
b 16585
 
7.2%
g 11305
 
4.9%
w 10370
 
4.5%
c 9954
 
4.3%
k 9885
 
4.3%
Other values (20) 62245
27.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 230781
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 29764
12.9%
l 23319
 
10.1%
r 21709
 
9.4%
i 17915
 
7.8%
a 17730
 
7.7%
b 16585
 
7.2%
g 11305
 
4.9%
w 10370
 
4.5%
c 9954
 
4.3%
k 9885
 
4.3%
Other values (20) 62245
27.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 230781
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 29764
12.9%
l 23319
 
10.1%
r 21709
 
9.4%
i 17915
 
7.8%
a 17730
 
7.7%
b 16585
 
7.2%
g 11305
 
4.9%
w 10370
 
4.5%
c 9954
 
4.3%
k 9885
 
4.3%
Other values (20) 62245
27.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 230781
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 29764
12.9%
l 23319
 
10.1%
r 21709
 
9.4%
i 17915
 
7.8%
a 17730
 
7.7%
b 16585
 
7.2%
g 11305
 
4.9%
w 10370
 
4.5%
c 9954
 
4.3%
k 9885
 
4.3%
Other values (20) 62245
27.0%

interior
Categorical

Imbalance 

Distinct17
Distinct (%)< 0.1%
Missing54
Missing (%)0.1%
Memory size390.8 KiB
black
21770 
gray
16146 
beige
5421 
tan
3875 
 
1505
Other values (12)
 
1229

Length

Max length9
Median length5
Mean length4.4007528
Min length1

Characters and Unicode

Total characters219800
Distinct characters23
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowblack
2nd rowblack
3rd rowgray
4th rowgray
5th rowgray

Common Values

ValueCountFrequency (%)
black 21770
43.5%
gray 16146
32.3%
beige 5421
 
10.8%
tan 3875
 
7.8%
1505
 
3.0%
brown 736
 
1.5%
red 130
 
0.3%
silver 100
 
0.2%
blue 86
 
0.2%
off-white 38
 
0.1%
Other values (7) 139
 
0.3%
(Missing) 54
 
0.1%

Length

2025-06-06T12:07:40.707311image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
black 21770
43.6%
gray 16146
32.3%
beige 5421
 
10.9%
tan 3875
 
7.8%
1505
 
3.0%
brown 736
 
1.5%
red 130
 
0.3%
silver 100
 
0.2%
blue 86
 
0.2%
off-white 38
 
0.1%
Other values (7) 139
 
0.3%

Most occurring characters

ValueCountFrequency (%)
a 41804
19.0%
b 28034
12.8%
l 22029
10.0%
c 21770
9.9%
k 21770
9.9%
g 21649
9.8%
r 17193
7.8%
y 16172
 
7.4%
e 11298
 
5.1%
i 5580
 
2.5%
Other values (13) 12501
 
5.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 219800
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 41804
19.0%
b 28034
12.8%
l 22029
10.0%
c 21770
9.9%
k 21770
9.9%
g 21649
9.8%
r 17193
7.8%
y 16172
 
7.4%
e 11298
 
5.1%
i 5580
 
2.5%
Other values (13) 12501
 
5.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 219800
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 41804
19.0%
b 28034
12.8%
l 22029
10.0%
c 21770
9.9%
k 21770
9.9%
g 21649
9.8%
r 17193
7.8%
y 16172
 
7.4%
e 11298
 
5.1%
i 5580
 
2.5%
Other values (13) 12501
 
5.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 219800
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 41804
19.0%
b 28034
12.8%
l 22029
10.0%
c 21770
9.9%
k 21770
9.9%
g 21649
9.8%
r 17193
7.8%
y 16172
 
7.4%
e 11298
 
5.1%
i 5580
 
2.5%
Other values (13) 12501
 
5.7%

seller
Text

Distinct5350
Distinct (%)10.7%
Missing0
Missing (%)0.0%
Memory size390.8 KiB
2025-06-06T12:07:40.990127image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Length

Max length50
Median length41
Mean length23.02654
Min length3

Characters and Unicode

Total characters1151327
Distinct characters46
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2495 ?
Unique (%)5.0%

Sample

1st rowlease plan usa
2nd rowinfiniti of montclair
3rd rowdm northwest inc
4th rowlexus of tacoma at fife
5th rowkia motors america inc
ValueCountFrequency (%)
inc 7817
 
4.6%
services 4332
 
2.6%
credit 4293
 
2.5%
motor 4258
 
2.5%
auto 4240
 
2.5%
corporation 4209
 
2.5%
llc 4164
 
2.5%
financial 4039
 
2.4%
ford 3336
 
2.0%
remarketing 3177
 
1.9%
Other values (4101) 124782
74.0%
2025-06-06T12:07:41.427409image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
120287
 
10.4%
e 102583
 
8.9%
a 93940
 
8.2%
r 86156
 
7.5%
n 85508
 
7.4%
i 81984
 
7.1%
o 77318
 
6.7%
t 71096
 
6.2%
c 66088
 
5.7%
s 60198
 
5.2%
Other values (36) 306169
26.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1151327
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
120287
 
10.4%
e 102583
 
8.9%
a 93940
 
8.2%
r 86156
 
7.5%
n 85508
 
7.4%
i 81984
 
7.1%
o 77318
 
6.7%
t 71096
 
6.2%
c 66088
 
5.7%
s 60198
 
5.2%
Other values (36) 306169
26.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1151327
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
120287
 
10.4%
e 102583
 
8.9%
a 93940
 
8.2%
r 86156
 
7.5%
n 85508
 
7.4%
i 81984
 
7.1%
o 77318
 
6.7%
t 71096
 
6.2%
c 66088
 
5.7%
s 60198
 
5.2%
Other values (36) 306169
26.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1151327
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
120287
 
10.4%
e 102583
 
8.9%
a 93940
 
8.2%
r 86156
 
7.5%
n 85508
 
7.4%
i 81984
 
7.1%
o 77318
 
6.7%
t 71096
 
6.2%
c 66088
 
5.7%
s 60198
 
5.2%
Other values (36) 306169
26.6%

sellingprice
Real number (ℝ)

High correlation 

Distinct971
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13667.282
Minimum1
Maximum159000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size390.8 KiB
2025-06-06T12:07:41.571050image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1500
Q16900
median12200
Q318300
95-th percentile30700
Maximum159000
Range158999
Interquartile range (IQR)11400

Descriptive statistics

Standard deviation9841.3572
Coefficient of variation (CV)0.72006691
Kurtosis11.629154
Mean13667.282
Median Absolute Deviation (MAD)5700
Skewness2.0358219
Sum6.8336408 × 108
Variance96852312
MonotonicityNot monotonic
2025-06-06T12:07:41.711649image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12000 413
 
0.8%
11000 399
 
0.8%
14000 396
 
0.8%
10000 384
 
0.8%
13000 373
 
0.7%
11500 362
 
0.7%
10500 331
 
0.7%
12500 328
 
0.7%
9000 320
 
0.6%
11800 307
 
0.6%
Other values (961) 46387
92.8%
ValueCountFrequency (%)
1 1
 
< 0.1%
150 3
 
< 0.1%
175 1
 
< 0.1%
200 21
 
< 0.1%
225 10
 
< 0.1%
250 27
 
0.1%
275 13
 
< 0.1%
300 113
0.2%
325 17
 
< 0.1%
350 61
0.1%
ValueCountFrequency (%)
159000 1
< 0.1%
158000 1
< 0.1%
147000 1
< 0.1%
141000 1
< 0.1%
139000 1
< 0.1%
134500 1
< 0.1%
131000 1
< 0.1%
123000 1
< 0.1%
120000 1
< 0.1%
117500 1
< 0.1%
Distinct2321
Distinct (%)4.6%
Missing0
Missing (%)0.0%
Memory size390.8 KiB
2025-06-06T12:07:41.991114image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Length

Max length39
Median length39
Mean length38.99864
Min length5

Characters and Unicode

Total characters1949932
Distinct characters40
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique594 ?
Unique (%)1.2%

Sample

1st rowWed Feb 04 2015 02:30:00 GMT-0800 (PST)
2nd rowTue Jun 16 2015 05:00:00 GMT-0700 (PDT)
3rd rowThu Jan 15 2015 03:00:00 GMT-0800 (PST)
4th rowWed Feb 11 2015 05:20:00 GMT-0800 (PST)
5th rowFri Mar 06 2015 04:00:00 GMT-0800 (PST)
ValueCountFrequency (%)
2015 45155
 
12.9%
pst 35500
 
10.1%
gmt-0800 35500
 
10.1%
wed 14722
 
4.2%
tue 14662
 
4.2%
feb 14602
 
4.2%
pdt 14498
 
4.1%
gmt-0700 14498
 
4.1%
thu 13868
 
4.0%
jan 12657
 
3.6%
Other values (261) 134326
38.4%
2025-06-06T12:07:42.401705image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 431242
22.1%
299988
15.4%
T 128526
 
6.6%
: 99996
 
5.1%
1 94946
 
4.9%
2 86056
 
4.4%
M 60213
 
3.1%
5 59080
 
3.0%
) 49998
 
2.6%
G 49998
 
2.6%
Other values (30) 589889
30.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1949932
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 431242
22.1%
299988
15.4%
T 128526
 
6.6%
: 99996
 
5.1%
1 94946
 
4.9%
2 86056
 
4.4%
M 60213
 
3.1%
5 59080
 
3.0%
) 49998
 
2.6%
G 49998
 
2.6%
Other values (30) 589889
30.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1949932
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 431242
22.1%
299988
15.4%
T 128526
 
6.6%
: 99996
 
5.1%
1 94946
 
4.9%
2 86056
 
4.4%
M 60213
 
3.1%
5 59080
 
3.0%
) 49998
 
2.6%
G 49998
 
2.6%
Other values (30) 589889
30.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1949932
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 431242
22.1%
299988
15.4%
T 128526
 
6.6%
: 99996
 
5.1%
1 94946
 
4.9%
2 86056
 
4.4%
M 60213
 
3.1%
5 59080
 
3.0%
) 49998
 
2.6%
G 49998
 
2.6%
Other values (30) 589889
30.3%

Interactions

2025-06-06T12:07:35.397772image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-06-06T12:07:34.150860image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-06-06T12:07:34.562585image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-06-06T12:07:34.979675image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-06-06T12:07:35.506196image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-06-06T12:07:34.255881image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-06-06T12:07:34.663427image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-06-06T12:07:35.087175image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-06-06T12:07:35.609665image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-06-06T12:07:34.354455image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-06-06T12:07:34.761634image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-06-06T12:07:35.191288image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-06-06T12:07:35.723695image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-06-06T12:07:34.453204image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-06-06T12:07:34.862607image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-06-06T12:07:35.287189image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Correlations

2025-06-06T12:07:42.502640image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
colorconditioninteriorodometersellingpricestatetransmissionyear
color1.0000.0560.1270.0750.0610.3220.7100.096
condition0.0561.0000.059-0.4050.4830.0890.0340.387
interior0.1270.0591.0000.0990.0730.0990.2300.109
odometer0.075-0.4050.0991.000-0.7050.0990.020-0.818
sellingprice0.0610.4830.073-0.7051.0000.0680.0170.678
state0.3220.0890.0990.0990.0681.0000.7090.094
transmission0.7100.0340.2300.0200.0170.7091.0000.070
year0.0960.3870.109-0.8180.6780.0940.0701.000

Missing values

2025-06-06T12:07:35.893823image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
A simple visualization of nullity by column.
2025-06-06T12:07:36.149579image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-06-06T12:07:36.426721image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

yearmakemodeltrimbodytransmissionvinstateconditionodometercolorinteriorsellersellingpricesaledate
02013FordFusionSESedanautomatic3fa6p0h71dr236627wi47.017548.0whiteblacklease plan usa14200Wed Feb 04 2015 02:30:00 GMT-0800 (PST)
12013InfinitiJXJX35SUVautomatic5n1al0mn8dc329790ca43.022035.0grayblackinfiniti of montclair31000Tue Jun 16 2015 05:00:00 GMT-0700 (PDT)
22009JeepGrand CherokeeLaredoSUVautomatic1j8gr48k69c529636ne34.095559.0silvergraydm northwest inc11600Thu Jan 15 2015 03:00:00 GMT-0800 (PST)
32008FordF-350 Super DutyXLTCrew Cabautomatic1ftww31rx8ed43632wa43.0135870.0whitegraylexus of tacoma at fife15400Wed Feb 11 2015 05:20:00 GMT-0800 (PST)
42014KiaSportageLXSUVautomatickndpbcac3e7642651nv44.013604.0whitegraykia motors america inc18500Fri Mar 06 2015 04:00:00 GMT-0800 (PST)
52012ToyotaTacomaV6Double Cabautomatic5tflu4en0cx040958tx35.033131.0silvergraycantu chevrolet26700Wed Feb 04 2015 02:00:00 GMT-0800 (PST)
62006JeepGrand CherokeeLaredosuvautomatic1j4gr48k66c280848nj29.0100783.0redgrayramsey volvo6400Tue Jun 16 2015 03:00:00 GMT-0700 (PDT)
72012NissanAltima2.5 SSedanautomatic1n4al2ap8cc124254nj37.028514.0whiteblacknissan-infiniti lt14400Wed Dec 31 2014 09:30:00 GMT-0800 (PST)
82013NissanMaxima3.5 SSedanautomatic1n4aa5ap7dc800119nc41.050662.0silverblackregional acceptance corporation / greenville13900Mon Jan 12 2015 09:30:00 GMT-0800 (PST)
92003NissanPathfinderLESUVNaNjn8dr09x03w714062fl19.0170588.0beigebeigecoral springs honda2300Fri Jan 09 2015 09:30:00 GMT-0800 (PST)
yearmakemodeltrimbodytransmissionvinstateconditionodometercolorinteriorsellersellingpricesaledate
499902004AcuraTL3.2Sedanautomatic19uua662x4a008912ca24.0113649.0grayblacksouth coast acura7400Wed Feb 11 2015 04:30:00 GMT-0800 (PST)
499912012ChevroletCamaroSSCoupemanual2g1fs1ew3c9100412ga41.017831.0silverblacktdaf remarketing21500Thu Feb 05 2015 02:00:00 GMT-0800 (PST)
499922012LincolnMKZBaseSedanautomatic3lnhl2gc3cr815962pa32.026459.0graybeigeford motor credit company llc14300Wed Jan 14 2015 01:00:00 GMT-0800 (PST)
499931997CadillacCateraBaseSedanautomaticw06vr54r3vr162162fl2.093631.0goldbeigecourtesy kia of brandon300Thu Jan 15 2015 08:20:00 GMT-0800 (PST)
499942014ChryslerTown and CountryTouringMinivanautomatic2c4rc1bg9er158574ga48.048836.0blackbeigeenterprise veh exchange/rental16900Tue Feb 03 2015 01:30:00 GMT-0800 (PST)
499952013FordF-150XLTSuperCrewautomatic1ftew1cm2dkf73954md46.031313.0redgrayars/enterprise22600Tue Feb 03 2015 01:30:00 GMT-0800 (PST)
499962012NissanSentra2Sedanautomatic3n1ab6ap6cl754270ca39.023385.0graygraymetro nissan of montclair9700Thu Jan 22 2015 04:00:00 GMT-0800 (PST)
499972012ToyotaHighlanderBaseSUVautomatic5tdzk3eh5cs064330nc39.075549.0blacktanfleet lease remarketing18400Mon Jan 26 2015 01:30:00 GMT-0800 (PST)
499982007ChryslerAspenLimitedSUVautomatic1a8hx58n57f585637ca19.078352.0blackgraya l financial9100Wed Jan 21 2015 04:30:00 GMT-0800 (PST)
499992007ChevroletImpalaLSsedanautomatic2g1wb58k579369419fl38.0109181.0beigebeigedt credit corporation5400Wed May 27 2015 08:25:00 GMT-0700 (PDT)